NeurIPS 2021

2,334 papers

Panoptic 3D Scene Reconstruction From a Single RGB Image

Manuel Dahnert, Ji Hou, Matthias Niessner, Angela Dai

Memory-Efficient Approximation Algorithms for Max-k-Cut and Correlation Clustering

Nimita Shinde, Vishnu Narayanan, James Saunderson

Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery

Huaxiu Yao, Ying Wei, Long-Kai Huang, Ding Xue, Junzhou Huang

Towards Deeper Deep Reinforcement Learning with Spectral Normalization

Nils Bjorck, Carla P. Gomes, Kilian Q. Weinberger

Learning Markov State Abstractions for Deep Reinforcement Learning

Cameron Allen, Neev Parikh, Omer Gottesman, George Konidaris

K-level Reasoning for Zero-Shot Coordination in Hanabi

Brandon Cui, Hengyuan Hu, Luis Pineda, Jakob Foerster

Gradual Domain Adaptation without Indexed Intermediate Domains

Hong-You Chen, Wei-Lun Chao

Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces

Aryan Deshwal, Jana Doppa

Distributed Saddle-Point Problems Under Data Similarity

Aleksandr Beznosikov, Gesualdo Scutari, Alexander Rogozin, Alexander Gasnikov

Fair Classification with Adversarial Perturbations

L. Elisa Celis, Anay Mehrotra, Nisheeth Vishnoi

Approximate optimization of convex functions with outlier noise

Anindya De, Sanjeev Khanna, Huan Li, MohammadHesam NikpeySalekde

Rethinking gradient sparsification as total error minimization

Atal Sahu, Aritra Dutta, Ahmed M. Abdelmoniem, Trambak Banerjee, Marco Canini

Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and Learning

Alberto Maria Metelli, Alessio Russo, Marcello Restelli

An Exact Characterization of the Generalization Error for the Gibbs Algorithm

Gholamali Aminian, Yuheng Bu, Laura Toni, Miguel Rodrigues, Gregory Wornell

How Data Augmentation affects Optimization for Linear Regression

Boris Hanin, Yi Sun

GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement

Martin Engelcke, Oiwi Parker Jones, Ingmar Posner

A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models

Ibrahim M Alabdulmohsin, Mario Lucic

Towards Enabling Meta-Learning from Target Models

Su Lu, Han-Jia Ye, Le Gan, De-Chuan Zhan

Relational Self-Attention: What's Missing in Attention for Video Understanding

Manjin Kim, Heeseung Kwon, CHUNYU WANG, Suha Kwak, Minsu Cho

When Is Generalizable Reinforcement Learning Tractable?

Dhruv Malik, Yuanzhi Li, Pradeep K Ravikumar, Pradeep K. Ravikumar

KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support

Pierre Glaser, Michael Arbel, Arthur Gretton

Learning to Elect

Cem Anil, Xuchan Bao

The Complexity of Sparse Tensor PCA

Davin Choo, Tommaso d'Orsi

Open-set Label Noise Can Improve Robustness Against Inherent Label Noise

Hongxin Wei, Lue Tao, RENCHUNZI XIE, Bo An

Label consistency in overfitted generalized k-means

Linfan Zhang, Arash Amini

Covariance-Aware Private Mean Estimation Without Private Covariance Estimation

Gavin Brown, Marco Gaboardi, Adam Smith, Jonathan Ullman, Lydia Zakynthinou

Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent

Spencer Frei, Quanquan Gu

Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation

Soojung Yang, Doyeong Hwang, Seul Lee, Seongok Ryu, Sung Ju Hwang

Finding Bipartite Components in Hypergraphs

Peter Macgregor, He Sun

Neural Ensemble Search for Uncertainty Estimation and Dataset Shift

Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris C Holmes, Frank Hutter

Distributed Deep Learning In Open Collaborations

Michael Diskin, Alexey Bukhtiyarov, Max Ryabinin, Lucile Saulnier, quentin lhoest

Grammar-Based Grounded Lexicon Learning

Jiayuan Mao, Freda Shi, Jiajun Wu, Roger P. Levy, Josh Tenenbaum

The future is log-Gaussian: ResNets and their infinite-depth-and-width limit at initialization

Mufan Li, Mihai Nica, Dan Roy

Neural Scene Flow Prior

Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey

Multi-Agent Reinforcement Learning in Stochastic Networked Systems

Yiheng Lin, Guannan Qu, Longbo Huang, Adam Wierman

One More Step Towards Reality: Cooperative Bandits with Imperfect Communication

Udari Madhushani, Abhimanyu Dubey, Naomi Leonard, Alex Pentland

On the Expressivity of Markov Reward

David Abel, Will Dabney, Anna Harutyunyan, Mark K Ho, Michael L. Littman

Challenges and Opportunities in High Dimensional Variational Inference

Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael R Andersen, Jonathan Huggins

Proper Value Equivalence

Christopher Grimm, Andre Barreto, Greg Farquhar, David Silver, Satinder P. Singh

To The Point: Correspondence-driven monocular 3D category reconstruction

Filippos Kokkinos, Iasonas Kokkinos

Cortico-cerebellar networks as decoupling neural interfaces

Joseph Pemberton, Ellen Boven, Richard Apps, Rui Ponte Costa

Forster Decomposition and Learning Halfspaces with Noise

Ilias Diakonikolas, Daniel Kane, Christos Tzamos

Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimization

Bing-Jing Hsieh, Ping-Chun Hsieh, Xi Liu

Going Beyond Linear Transformers with Recurrent Fast Weight Programmers

Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber

Joint Modeling of Visual Objects and Relations for Scene Graph Generation

Minghao Xu, Meng Qu, Bingbing Ni, Jian Tang

Near-Optimal Offline Reinforcement Learning via Double Variance Reduction

Ming Yin, Yu Bai, Yu-Xiang Wang

SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL

Ruichu Cai, Jinjie Yuan, Boyan Xu, Zhifeng Hao

Random Noise Defense Against Query-Based Black-Box Attacks

Zeyu Qin, Yanbo Fan, Hongyuan Zha, Baoyuan Wu

Robustness of Graph Neural Networks at Scale

Simon Geisler, Tobias Schmidt, Hakan Şirin, Daniel Zügner, Aleksandar Bojchevski

Improved Regret Bounds for Tracking Experts with Memory

James Robinson, Mark Herbster

A Provably Efficient Sample Collection Strategy for Reinforcement Learning

Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric

Variance-Aware Off-Policy Evaluation with Linear Function Approximation

Yifei Min, Tianhao Wang, Dongruo Zhou, Quanquan Gu

Concentration inequalities under sub-Gaussian and sub-exponential conditions

Andreas Maurer, Massimiliano Pontil

Storchastic: A Framework for General Stochastic Automatic Differentiation

Emile Krieken, Jakub Tomczak, Annette Ten Teije

LEADS: Learning Dynamical Systems that Generalize Across Environments

Yuan Yin, Ibrahim Ayed, Emmanuel de Bézenac, Nicolas Baskiotis, Patrick Gallinari

One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval

Akari Asai, Xinyan Yu, Jungo Kasai, Hanna Hajishirzi

Edge Representation Learning with Hypergraphs

Jaehyeong Jo, Jinheon Baek, Seul Lee, Minki Kang, Sung Ju Hwang

A Gradient Method for Multilevel Optimization

Ryo Sato, Mirai Tanaka, Akiko Takeda

Neural Active Learning with Performance Guarantees

Zhilei Wang, Pranjal Awasthi, Christoph Dann, Ayush Sekhari, Claudio Gentile

Meta-learning with an Adaptive Task Scheduler

Huaxiu Yao, Yu Wang, Ying Wei, Peilin Zhao, Mehrdad Mahdavi

LSH-SMILE: Locality Sensitive Hashing Accelerated Simulation and Learning

Chonghao Sima, Yexiang Xue

NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem

Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang

Statistical Inference with M-Estimators on Adaptively Collected Data

Kelly Zhang, Lucas Janson, Susan Murphy

Distribution-free inference for regression: discrete, continuous, and in between

Yonghoon Lee, Rina Barber

Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble

Gaon An, Seungyong Moon, Jang-Hyun Kim, Hyun Oh Song

Directed Spectrum Measures Improve Latent Network Models Of Neural Populations

Neil Gallagher, Kafui Dzirasa, David E Carlson, David E. Carlson

On Optimal Robustness to Adversarial Corruption in Online Decision Problems

Shinji Ito

Deeply Shared Filter Bases for Parameter-Efficient Convolutional Neural Networks

Woochul Kang, Daeyeon Kim

Regret Bounds for Gaussian-Process Optimization in Large Domains

Manuel Wuethrich, Bernhard Schölkopf, Andreas Krause

A Normative and Biologically Plausible Algorithm for Independent Component Analysis

Yanis Bahroun, Dmitri B. Chklovskii, Anirvan Sengupta

SOAT: A Scene- and Object-Aware Transformer for Vision-and-Language Navigation

Abhinav Moudgil, Arjun Majumdar, Harsh Agrawal, Stefan Lee, Dhruv Batra

Indexed Minimum Empirical Divergence for Unimodal Bandits

Hassan SABER, Pierre Menard, Odalric-ambrym Maillard

An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning

Blake E Woodworth, Nathan Srebro

Shape your Space: A Gaussian Mixture Regularization Approach to Deterministic Autoencoders

Amrutha Saseendran, Kathrin Skubch, Stefan Falkner, Margret Keuper

Weak-shot Fine-grained Classification via Similarity Transfer

Junjie Chen, Li Niu, Liu Liu, Liqing Zhang

Manifold Topology Divergence: a Framework for Comparing Data Manifolds.

Serguei Barannikov, Ilya Trofimov, Grigorii Sotnikov, Ekaterina Trimbach, Alexander Korotin

HRFormer: High-Resolution Vision Transformer for Dense Predict

YUHUI YUAN, Rao Fu, Lang Huang, Weihong Lin, Chao Zhang

Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification

Jiong Zhang, Wei-Cheng Chang, Hsiang-Fu Yu, Inderjit S Dhillon, Inderjit S. Dhillon

Towards Tight Communication Lower Bounds for Distributed Optimisation

Janne H. Korhonen, Dan Alistarh

Faster Non-asymptotic Convergence for Double Q-learning

Lin Zhao, Huaqing Xiong, Yingbin Liang

Evaluating Gradient Inversion Attacks and Defenses in Federated Learning

Yangsibo Huang, Samyak Gupta, Zhao Song, Kai Li, Sanjeev Arora

Sharp Impossibility Results for Hyper-graph Testing

Jiashun Jin, Zheng Tracy Ke, Jiajun Liang

Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose

Angtian Wang, Shenxiao Mei, Alan L. Yuille, Adam Kortylewski

Online Robust Reinforcement Learning with Model Uncertainty

Yue Wang, Shaofeng Zou

On the Value of Infinite Gradients in Variational Autoencoder Models

Bin Dai, Li Wenliang, David P. Wipf

Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks

Jesse Hagenaars, Federico Paredes-Valles, Guido de Croon

Learning to Draw: Emergent Communication through Sketching

Daniela Mihai, Jonathon Hare

Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective

Zhengzhuo Xu, Zenghao Chai, Chun Yuan

Information-constrained optimization: can adaptive processing of gradients help?

Jayadev Acharya, Clement Canonne, Prathamesh Mayekar, Himanshu Tyagi

Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks

Iulia Duta, Andrei Nicolicioiu, Marius Leordeanu

BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery

Chris Cundy, Aditya Grover, Stefano Ermon

ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning

Hyuck Lee, Seungjae Shin, Heeyoung Kim

Exploring the Limits of Out-of-Distribution Detection

Stanislav Fort, Jie Ren, Balaji Lakshminarayanan

Do Wider Neural Networks Really Help Adversarial Robustness?

Boxi Wu, Jinghui Chen, Deng Cai, Xiaofei He, Quanquan Gu

BooVI: Provably Efficient Bootstrapped Value Iteration

Boyi Liu, Qi Cai, Zhuoran Yang, Zhaoran Wang

Contrastive Learning of Global and Local Video Representations

Shuang Ma, Zhaoyang Zeng, Daniel McDuff, Yale Song

argmax centroid

Chengyue Gong, Mao Ye, Qiang Liu

Regulating algorithmic filtering on social media

Sarah Cen, Devavrat Shah

Adversarial Attacks on Graph Classifiers via Bayesian Optimisation

Xingchen Wan, Henry Kenlay, Robin Ru, Arno Blaas, Michael A Osborne

Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees

Gregory Dexter, Kevin Bello, Jean Honorio